The goal of this study is to identify low frequency and rare coding variants that have significant biomedical impact in Hispanic Americans and African Americans. Family-based linkage analysis has been a powerful tool for identification of genes contributing to monogenic disorders. Until recently family-based approaches have been of limited utility in complex trait genetics. Searches for common genetic variants associated with complex traits have been highly successful in Genome Wide Association Studies (GWAS). It is now widely recognized, however, that common variations frequently explain only a small part of the inter-individual variation in populations. For example, numerous cardiovascular disease (CVD), type 2 diabetes, and body mass genes have been identified, but these genes collectively only explain 10% or less of the heritability. There are several possible sources for the missing heritability. We have developed a powerful and highly efficient family-based method for identification of low frequency (LF) or rare variants which contribute significantly to phenotypic variation of complex traits in the Insulin Resistance Atherosclerosis Family Study (IRASFS). This method has been demonstrated with the identification of an LF (1.1% MAF) coding variant in the ADIPOQ (adiponectin) gene that reduces circulating adiponectin to <20% of normal in Hispanic Americans. This mutations accounts for 17% of the variance in plasma adiponectin in the entire population and accounts for the LOD score of 8.2 in linkage analysis. Based on these efforts, we hypothesize that LF and rare variants contribute substantially to the variance in CVD risk factors. We propose a combination of family-based linkage analyses, whole exome sequencing, and association analysis to identify LF/rare variants of large effect in novel genes that significantly influence a wide range of CVD risk factors. Comprehensive analysis of IRASFS Hispanic and African American families will be used to target chromosomal regions for detailed evaluation of exome sequence data. Families contributing to evidence of linkage at selected chromosomal locations will be assessed for significant coding variations. Importantly this approach enables the rapid interrogation of a wide range of CVD risk phenotypes including novel measures. Variants identified from the family-based approaches will be tested for association in the entire IRASFS sample and replicated in meta analysis of multiple Hispanic (n=6880) and African American (n=15,180) DNA samples to test the primary trait association and assess the influence of high effect variants on subclinical and clinical CVD.